/
detection.py
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/
detection.py
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import torch
from torch.autograd import Function
from ..box import decode, nms, diounms
def intersect(box_a, box_b):
n = box_a.size(0)
A = box_a.size(1)
B = box_b.size(1)
max_xy = torch.min(box_a[:, :, 2:].unsqueeze(2).expand(n, A, B, 2),
box_b[:, :, 2:].unsqueeze(1).expand(n, A, B, 2))
min_xy = torch.max(box_a[:, :, :2].unsqueeze(2).expand(n, A, B, 2),
box_b[:, :, :2].unsqueeze(1).expand(n, A, B, 2))
inter = torch.clamp((max_xy - min_xy), min=0)
return inter[:, :, :, 0] * inter[:, :, :, 1]
def jaccard(box_a, box_b, iscrowd:bool=False):
use_batch = True
if box_a.dim() == 2:
use_batch = False
box_a = box_a[None, ...]
box_b = box_b[None, ...]
inter = intersect(box_a, box_b)
area_a = ((box_a[:, :, 2]-box_a[:, :, 0]) *
(box_a[:, :, 3]-box_a[:, :, 1])).unsqueeze(2).expand_as(inter) # [A,B]
area_b = ((box_b[:, :, 2]-box_b[:, :, 0]) *
(box_b[:, :, 3]-box_b[:, :, 1])).unsqueeze(1).expand_as(inter) # [A,B]
union = area_a + area_b - inter
out = inter / area_a if iscrowd else inter / union
return out if use_batch else out.squeeze(0)
def box_diou(boxes1, boxes2, beta):
def box_area(box):
# box = 4xn
return (box[2] - box[0]) * (box[3] - box[1])
area1 = box_area(boxes1.t())
area2 = box_area(boxes2.t())
lt = torch.max(boxes1[:, None, :2], boxes2[:, :2]) # [N,M,2]
rb = torch.min(boxes1[:, None, 2:], boxes2[:, 2:]) # [N,M,2]
clt=torch.min(boxes1[:, None, :2], boxes2[:, :2])
crb=torch.max(boxes1[:, None, 2:], boxes2[:, 2:])
x1=(boxes1[:, None, 0] + boxes1[:, None, 2])/2
y1=(boxes1[:, None, 1] + boxes1[:, None, 3])/2
x2=(boxes2[:, None, 0] + boxes2[:, None, 2])/2
y2=(boxes2[:, None, 1] + boxes2[:, None, 3])/2
d=(x1-x2.t())**2 + (y1-y2.t())**2
c=((crb-clt)**2).sum(dim=2)
inter = (rb - lt).clamp(min=0).prod(2) # [N,M]
return inter / (area1[:, None] + area2 - inter) - (d / c) ** beta # iou = inter / (area1 + area2 - inter)
class Detect(Function):
"""At test time, Detect is the final layer of SSD. Decode location preds,
apply non-maximum suppression to location predictions based on conf
scores and threshold to a top_k number of output predictions for both
confidence score and locations.
"""
def __init__(self, num_classes, bkg_label, top_k, conf_thresh, nms_thresh,variance, nms_kind, beta1):
self.num_classes = num_classes
self.background_label = bkg_label
self.top_k = top_k
# Parameters used in nms.
self.nms_thresh = nms_thresh
if nms_thresh <= 0:
raise ValueError('nms_threshold must be non negative.')
self.conf_thresh = conf_thresh
self.variance = variance
self.nms_kind = nms_kind
self.beta1 = beta1
def forward(self, loc_data, conf_data, prior_data):
"""
Args:
loc_data: (tensor) Loc preds from loc layers
Shape: [batch,num_priors*4]
conf_data: (tensor) Shape: Conf preds from conf layers
Shape: [batch*num_priors,num_classes]
prior_data: (tensor) Prior boxes and variances from priorbox layers
Shape: [1,num_priors,4]
nms_kind: greedynms or diounms
"""
num = loc_data.size(0)
num_priors = prior_data.size(0)
output = torch.zeros(num, self.num_classes, self.top_k, 5)
conf_preds = conf_data.view(num, num_priors,
self.num_classes).transpose(2, 1)
# Decode predictions into bboxes.
for i in range(num):
decoded_boxes = decode(loc_data[i], prior_data, self.variance)
# For each class, perform nms
conf_scores = conf_preds[i].clone()
sort_scores, idx = conf_scores.sort(1, descending=True)
c_mask = (sort_scores>=self.conf_thresh)[:,:self.top_k]
s1,s2 = decoded_boxes.size()
z = decoded_boxes[idx]
h = (torch.arange(0,21).cuda()).float().unsqueeze(1).unsqueeze(1)
one = torch.ones(21,s1,s2).cuda().mul(h)
boxes = z[:,:self.top_k][c_mask] #[N,4] box
z = one*2 + z
boxes_batch = z[:,:self.top_k][c_mask] #[N,4] box with offset
scores = sort_scores[:,:self.top_k][c_mask] #[N,1]
classes = one[:,:self.top_k][c_mask][:,0] #[N,1]
# Do not support Fast NMS, due to it damages the performance.
if self.nms_kind == "cluster_nms" or self.nms_kind == "cluster_weighted_nms" :
iou = jaccard(boxes_batch, boxes_batch).triu_(diagonal=1)
else:
if self.nms_kind == "cluster_diounms" or self.nms_kind == "cluster_weighted_diounms":
iou = box_diou(boxes_batch, boxes_batch, self.beta1).triu_(diagonal=1)
else:
assert Exception("Currently, NMS only surports 'cluster_nms', 'cluster_diounms', 'cluster_weighted_nms', 'cluster_weighted_diounms'.")
B = iou
for j in range(999):
A=B
maxA=A.max(dim=0)[0]
E = (maxA<self.nms_thresh).float().unsqueeze(1).expand_as(A)
B=iou.mul(E)
if A.equal(B)==True:
break
keep = maxA < self.nms_thresh
if self.nms_kind == "cluster_weighted_nms" or self.nms_kind == "cluster_weighted_diounms":
n = len(scores)
weights = (B*(B>0.8).float() + torch.eye(n).cuda()) * (scores.reshape((1,n)))
xx1 = boxes[:,0].expand(n,n)
yy1 = boxes[:,1].expand(n,n)
xx2 = boxes[:,2].expand(n,n)
yy2 = boxes[:,3].expand(n,n)
weightsum=weights.sum(dim=1)
xx1 = (xx1*weights).sum(dim=1)/(weightsum)
yy1 = (yy1*weights).sum(dim=1)/(weightsum)
xx2 = (xx2*weights).sum(dim=1)/(weightsum)
yy2 = (yy2*weights).sum(dim=1)/(weightsum)
boxes = torch.stack([xx1, yy1, xx2, yy2], 1)
boxes = boxes[keep]
scores = scores[keep]
classes = classes[keep]
score_box = torch.cat((scores.unsqueeze(1),boxes), 1)
for cl in range(1, self.num_classes):
mask = (classes == cl)
output[i, cl, :]=torch.cat((score_box[mask],output[i, cl, :]),0)[:self.top_k]
return output
def forward_traditional_nms(self, loc_data, conf_data, prior_data):
"""
Args:
loc_data: (tensor) Loc preds from loc layers
Shape: [batch,num_priors*4]
conf_data: (tensor) Shape: Conf preds from conf layers
Shape: [batch*num_priors,num_classes]
prior_data: (tensor) Prior boxes and variances from priorbox layers
Shape: [1,num_priors,4]
nms_kind: greedynms or diounms
"""
# This funtion is no longer supported. Due to extremely time-consuming.
num = loc_data.size(0)
num_priors = prior_data.size(0)
output = torch.zeros(num, self.num_classes, self.top_k, 5)
conf_preds = conf_data.view(num, num_priors,
self.num_classes).transpose(2, 1)
# Decode predictions into bboxes.
for i in range(num):
decoded_boxes = decode(loc_data[i], prior_data, self.variance)
# For each class, perform nms
conf_scores = conf_preds[i].clone()
for cl in range(1, self.num_classes):
c_mask = conf_scores[cl].gt(self.conf_thresh)
scores = conf_scores[cl][c_mask]
if scores.size(0) == 0:
continue
l_mask = c_mask.unsqueeze(1).expand_as(decoded_boxes)
boxes = decoded_boxes[l_mask].view(-1, 4)
# idx of highest scoring and non-overlapping boxes per class
if self.nms_kind == "greedynms":
ids, count = diounms(boxes, scores, self.nms_thresh, self.top_k)
else:
if self.nms_kind == "diounms":
ids, count = diounms(boxes, scores, self.nms_thresh, self.top_k, self.beta1)
else:
print("use default greedy-NMS")
ids, count = nms(boxes, scores, self.nms_thresh, self.top_k)
output[i, cl, :count] = \
torch.cat((scores[ids[:count]].unsqueeze(1),
boxes[ids[:count]]), 1)
flt = output.contiguous().view(num, -1, 5)
_, idx = flt[:, :, 0].sort(1, descending=True)
_, rank = idx.sort(1)
flt[(rank < self.top_k).unsqueeze(-1).expand_as(flt)].fill_(0)
return output